Ab Initio & Template Free Structure Prediction — Hands-on

Learn how to perform template free (ab initio) protein structure prediction using modern AI based pipelines and classical approaches. From preparing sequence inputs and MSAs, running AlphaFold like tools and related servers, through interpreting confidence metrics and comparing with experimental structures, you will build models that are realistically assessed, documented and ready for cautious use in docking, MD and functional hypotheses.

Ab Initio & Template Free Structure Prediction

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Session Index

Session 1 — Concepts, Regimes & Input Preparation Session 2 — Running AI Based Prediction Pipelines Session 3 — Confidence, Error & Model Comparison Session 4 — Refinement, Use Cases & Caveats

Session 1

Fee: Rs 12320

Concepts, Regimes & Input Preparation

When and why template free prediction?

low homology and novel folds IDPs vs ordered domains monomers vs complexes overview

Sequences, domains and construct design

domain boundary identification signal peptides, TM segments low complexity and disordered regions

MSA and template search as features

multiple sequence alignments sequence covariation concepts structure templates in AI pipelines

Session 2

Fee: Rs 16520

Running AI Based Prediction Pipelines

Overview of modern prediction engines

AlphaFold style servers multiple implementations and forks colab style notebooks concepts

Inputs, options and model recycling

MSA depth and databases template on/off regimes number of recycles and model seeds

Interpreting raw outputs

ranked model sets pLDDT maps aligned error and PAE matrices

Session 3

Fee: Rs 20720

Confidence, Error & Model Comparison

Reading confidence scores and maps

pLDDT regimes PAE and inter domain confidence low confidence regions and disorder

Geometry and stereochemistry checks

Ramachandran and rotamers clash and packing assessments per residue quality profiles

Comparing to templates or experimental structures

RMSD and TM score ideas conserved core vs flexible loops binding site and motif alignment

Session 4

Fee: Rs 26320

Refinement, Use Cases & Caveats

Basic refinement and cleanup of predicted models

adding missing atoms and hydrogens limited energy minimization ideas removing highly unreliable segments

Using models for docking and MD with care

choosing confident regions defining binding site boxes interpretation limits for energetics

Reporting, caveats and integration with experiments

documenting inputs and settings figure panels with confidence coloring suggesting experiments to test models

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Ab Initio & Template Free Structure Prediction Training | AI Based Protein Modeling & Confidence Analysis

Ab Initio & Template Free Structure Prediction — Hands-on